import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None       )		->	List[str]:







 '''simple docstring'''
 # set parameter of one layer
 assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
 UpperCamelCase			  =      nn.Parameter(UpperCamelCase_       )
 if bias is not None:
  assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
  UpperCamelCase			  =      nn.Parameter(UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 # set torch weights for 1-to-1 comparison
 UpperCamelCase			  =      np.asarray(weights[0]       )
 UpperCamelCase			  =      np.asarray(weights[1]       )
 UpperCamelCase			  =      np.asarray(weights[2]       )

 set_param(
     torch_layer.self_attention.query_key							,					torch.tensor(UpperCamelCase_       ).transpose(1							,					2       ).contiguous().view(-1							,					UpperCamelCase_       )							,					)
 set_param(
     torch_layer.self_attention.value							,					torch.tensor(UpperCamelCase_       ).transpose(1							,					2       ).contiguous().view(-1							,					UpperCamelCase_       )							,					)
 set_param(
     torch_layer.output.dense							,					torch.tensor(UpperCamelCase_       ).view(-1							,					UpperCamelCase_       ).contiguous().transpose(0							,					1       )							,					)





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # set torch weights for 1-to-1 comparison
 UpperCamelCase			  =      np.asarray(weights[0]       )
 UpperCamelCase			  =      np.asarray(weights[1]       )
 UpperCamelCase			  =      np.asarray(weights[2]       )
 UpperCamelCase			  =      np.asarray(weights[3]       )

 set_param(
     torch_layer.self_attention.query							,					torch.tensor(UpperCamelCase_       ).transpose(1							,					2       ).contiguous().view(-1							,					UpperCamelCase_       )							,					)
 set_param(
     torch_layer.self_attention.key							,					torch.tensor(UpperCamelCase_       ).transpose(1							,					2       ).contiguous().view(-1							,					UpperCamelCase_       )							,					)
 set_param(
     torch_layer.self_attention.value							,					torch.tensor(UpperCamelCase_       ).transpose(1							,					2       ).contiguous().view(-1							,					UpperCamelCase_       )							,					)
 set_param(
     torch_layer.output.dense							,					torch.tensor(UpperCamelCase_       ).view(-1							,					UpperCamelCase_       ).contiguous().transpose(0							,					1       )							,					)





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	int:







 '''simple docstring'''
 # layernorm 1
 UpperCamelCase			  =      weights[0][0][0]
 UpperCamelCase			  =      np.asarray(layer_norm_a[0]       )
 UpperCamelCase			  =      np.asarray(layer_norm_a[1]       )
 set_param(
     torch_block.attention.layer_norm							,					torch.tensor(UpperCamelCase_       )							,					torch.tensor(UpperCamelCase_       )							,					)

 # lsh weights + output
 UpperCamelCase			  =      weights[0][1]
 if len(UpperCamelCase_       ) < 4:
  set_layer_weights_in_torch_lsh(UpperCamelCase_							,					torch_block.attention							,					UpperCamelCase_       )
 else:
  set_layer_weights_in_torch_local(UpperCamelCase_							,					torch_block.attention							,					UpperCamelCase_       )

 # intermediate weighs
 UpperCamelCase			  =      weights[2][0][1][2]

 # Chunked Feed Forward
 if len(UpperCamelCase_       ) == 4:
  UpperCamelCase			  =      intermediate_weights[2]

 # layernorm 2
 UpperCamelCase			  =      np.asarray(intermediate_weights[0][0]       )
 UpperCamelCase			  =      np.asarray(intermediate_weights[0][1]       )
 set_param(
     torch_block.feed_forward.layer_norm							,					torch.tensor(UpperCamelCase_       )							,					torch.tensor(UpperCamelCase_       )							,					)

 # intermediate dense
 UpperCamelCase			  =      np.asarray(intermediate_weights[1][0]       )
 UpperCamelCase			  =      np.asarray(intermediate_weights[1][1]       )
 set_param(
     torch_block.feed_forward.dense.dense							,					torch.tensor(UpperCamelCase_       ).transpose(0							,					1       ).contiguous()							,					torch.tensor(UpperCamelCase_       )							,					)

 # intermediate out
 UpperCamelCase			  =      np.asarray(intermediate_weights[4][0]       )
 UpperCamelCase			  =      np.asarray(intermediate_weights[4][1]       )
 set_param(
     torch_block.feed_forward.output.dense							,					torch.tensor(UpperCamelCase_       ).transpose(0							,					1       ).contiguous()							,					torch.tensor(UpperCamelCase_       )							,					)





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 # reformer model
 UpperCamelCase			  =      torch_model.reformer

 # word embeds
 UpperCamelCase			  =      np.asarray(weights[1]       )
 set_param(
     torch_model_reformer.embeddings.word_embeddings							,					torch.tensor(UpperCamelCase_       )							,					)

 if isinstance(weights[3]							,					UpperCamelCase_       ):
  UpperCamelCase			  =      torch_model_reformer.embeddings.position_embeddings
  for emb_idx in range(len(position_embeddings.weights       )       ):
   UpperCamelCase			  =      np.asarray(weights[3][emb_idx][0]       )
   assert (
       position_embeddings.weights[emb_idx].shape == emb_weights.shape
   ), f"""{position_embeddings[emb_idx]} emb does not match"""
   UpperCamelCase			  =      nn.Parameter(torch.tensor(UpperCamelCase_       )       )

 UpperCamelCase			  =      weights[5]
 assert len(torch_model_reformer.encoder.layers       ) * 4 == len(
     UpperCamelCase_       ), "HF and trax model do not have the same number of layers"
 for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers       ):
  UpperCamelCase			  =      trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
  set_block_weights_in_torch(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

 # output layer norm
 UpperCamelCase			  =      np.asarray(weights[7][0]       )
 UpperCamelCase			  =      np.asarray(weights[7][1]       )
 set_param(
     torch_model_reformer.encoder.layer_norm							,					torch.tensor(UpperCamelCase_       )							,					torch.tensor(UpperCamelCase_       )							,					)

 # output embeddings
 UpperCamelCase			  =      np.asarray(weights[9][0]       )
 UpperCamelCase			  =      np.asarray(weights[9][1]       )
 set_param(
     torch_model.lm_head.decoder							,					torch.tensor(UpperCamelCase_       ).transpose(0							,					1       ).contiguous()							,					torch.tensor(UpperCamelCase_       )							,					)





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      ReformerConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      ReformerModelWithLMHead(UpperCamelCase_       )

 with open(UpperCamelCase_							,					"""rb"""       ) as f:
  UpperCamelCase			  =      pickle.load(UpperCamelCase_       )["""weights"""]

 set_model_weights_in_torch(UpperCamelCase_							,					UpperCamelCase_							,					config.hidden_size       )

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained Reformer model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_SCREAMING_SNAKE_CASE 		=      get_tests_dir("""fixtures""")
_SCREAMING_SNAKE_CASE 		=      get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
_SCREAMING_SNAKE_CASE 		=      get_tests_dir("""fixtures/dummy-config.json""")
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      0







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""				)
    self.assertIsInstance(lowerCamelCase_   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_				)
    self.assertIsInstance(lowerCamelCase_   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    with tempfile.TemporaryDirectory() as tmpdirname:
     UpperCamelCase			  =      WavaVecaConfig()

     # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_				).to_dict()

     config_dict.pop("""feature_extractor_type"""				)
     UpperCamelCase			  =      WavaVecaFeatureExtractor(**lowerCamelCase_				)

     # save in new folder
     model_config.save_pretrained(lowerCamelCase_				)
     config.save_pretrained(lowerCamelCase_				)

     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_				)

     # make sure private variable is not incorrectly saved
     UpperCamelCase			  =      json.loads(config.to_json_string()				)
     self.assertTrue("""_processor_class""" not in dict_as_saved				)

    self.assertIsInstance(lowerCamelCase_   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_				)
    self.assertIsInstance(lowerCamelCase_   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    with self.assertRaisesRegex(
        lowerCamelCase_   ,   """bert-base is not a local folder and is not a valid model identifier"""				):
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained("""bert-base"""				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    with self.assertRaisesRegex(
        lowerCamelCase_   ,   R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""				):
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_   ,   revision="""aaaaaa"""				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    with self.assertRaisesRegex(
        lowerCamelCase_   ,   """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""   ,   ):
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model"""				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    with self.assertRaises(lowerCamelCase_				):
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""				)
    # If remote code is disabled, we can't load this config.
    with self.assertRaises(lowerCamelCase_				):
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""   ,   trust_remote_code=lowerCamelCase_				)

    UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
        """hf-internal-testing/test_dynamic_feature_extractor"""   ,   trust_remote_code=lowerCamelCase_				)
    self.assertEqual(feature_extractor.__class__.__name__   ,   """NewFeatureExtractor"""				)

    # Test feature extractor can be reloaded.
    with tempfile.TemporaryDirectory() as tmp_dir:
     feature_extractor.save_pretrained(lowerCamelCase_				)
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_   ,   trust_remote_code=lowerCamelCase_				)
    self.assertEqual(reloaded_feature_extractor.__class__.__name__   ,   """NewFeatureExtractor"""				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    try:
     AutoConfig.register("""custom"""   ,   lowerCamelCase_				)
     AutoFeatureExtractor.register(lowerCamelCase_   ,   lowerCamelCase_				)
     # Trying to register something existing in the Transformers library will raise an error
     with self.assertRaises(lowerCamelCase_				):
      AutoFeatureExtractor.register(lowerCamelCase_   ,   lowerCamelCase_				)

     # Now that the config is registered, it can be used as any other config with the auto-API
     UpperCamelCase			  =      CustomFeatureExtractor.from_pretrained(lowerCamelCase_				)
     with tempfile.TemporaryDirectory() as tmp_dir:
      feature_extractor.save_pretrained(lowerCamelCase_				)
      UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(lowerCamelCase_				)
      self.assertIsInstance(lowerCamelCase_   ,   lowerCamelCase_				)

    finally:
     if "custom" in CONFIG_MAPPING._extra_content:
      del CONFIG_MAPPING._extra_content["custom"]
     if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
      del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
       __lowerCAmelCase           =				True

    try:
     AutoConfig.register("""custom"""   ,   lowerCamelCase_				)
     AutoFeatureExtractor.register(lowerCamelCase_   ,   lowerCamelCase_				)
     # If remote code is not set, the default is to use local
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""				)
     self.assertEqual(feature_extractor.__class__.__name__   ,   """NewFeatureExtractor"""				)
     self.assertTrue(feature_extractor.is_local				)

     # If remote code is disabled, we load the local one.
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""   ,   trust_remote_code=lowerCamelCase_				)
     self.assertEqual(feature_extractor.__class__.__name__   ,   """NewFeatureExtractor"""				)
     self.assertTrue(feature_extractor.is_local				)

     # If remote is enabled, we load from the Hub
     UpperCamelCase			  =      AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""   ,   trust_remote_code=lowerCamelCase_				)
     self.assertEqual(feature_extractor.__class__.__name__   ,   """NewFeatureExtractor"""				)
     self.assertTrue(not hasattr(lowerCamelCase_   ,   """is_local"""				)				)

    finally:
     if "custom" in CONFIG_MAPPING._extra_content:
      del CONFIG_MAPPING._extra_content["custom"]
     if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
      del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
